Implement some basic backtesting.
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@@ -70,6 +70,9 @@ def get_nice_price_points(min_price, max_price):
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Generate a reasonable set of price points for the y-axis that look clean
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and cover the range without cluttering the chart.
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"""
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# Handle zero or negative prices
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min_price = max(min_price, 0.0001) # Set minimum price to $0.0001
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log_min = np.floor(np.log10(min_price))
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log_max = np.ceil(np.log10(max_price))
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price_points = []
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@@ -708,6 +711,206 @@ def visualize_cycle_patterns(df, cycle_returns, cycle_volatility):
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plt.close()
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def create_backtest_plot(
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df, backtest_date="2020-05-11", start_date="2012-11-28", project_days=1650
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):
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"""
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Create a plot comparing actual price history against model projections from a historical date.
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Args:
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df: DataFrame with historical price data
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backtest_date: Date to start the backtest from (default: third halving)
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start_date: Date to start considering historical data (default: first halving)
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project_days: Number of days to project forward from backtest date
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"""
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# Convert dates to datetime
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backtest_date = pd.to_datetime(backtest_date)
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start_date = pd.to_datetime(start_date)
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# Validate dates
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if start_date >= backtest_date:
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raise ValueError("start_date must be earlier than backtest_date")
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# Clean the data: remove rows with zero or invalid prices and filter by date
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df = df[(df["Close"] > 0) & (df["Date"] >= start_date)].copy()
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# Split data into training (before backtest date) and validation (after backtest date)
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training_df = df[df["Date"] <= backtest_date].copy()
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validation_df = df[df["Date"] > backtest_date].copy()
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# Check if we have enough data
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if len(training_df) < 30: # Require at least 30 days of training data
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raise ValueError("Insufficient training data before backtest date")
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# Generate historical projections using only training data
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historical_projections = project_prices_with_cycles(
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training_df, days_forward=project_days
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)
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# Set up the plot
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plt.style.use("seaborn-v0_8")
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fig, ax = plt.figure(figsize=(15, 10)), plt.gca()
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# Plot training data
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ax.semilogy(
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training_df["Date"],
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training_df["Close"],
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"b-",
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label=f'Historical Price (Training: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")})',
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alpha=0.7,
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)
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# Plot validation data
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ax.semilogy(
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validation_df["Date"],
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validation_df["Close"],
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"g-",
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label=f'Actual Price (Validation: {backtest_date.strftime("%Y-%m-%d")} onwards)',
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linewidth=2,
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)
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# Plot projections
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ax.semilogy(
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historical_projections.index,
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historical_projections["Expected_Trend"],
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"--",
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color="purple",
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label="Model Projection (Expected)",
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)
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ax.semilogy(
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historical_projections.index,
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historical_projections["Median"],
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":",
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color="orange",
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label="Model Projection (Median)",
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)
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# Add confidence intervals
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ax.fill_between(
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historical_projections.index,
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historical_projections["Lower_95"],
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historical_projections["Upper_95"],
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alpha=0.2,
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color="orange",
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label="95% Confidence Interval",
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)
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ax.fill_between(
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historical_projections.index,
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historical_projections["Lower_68"],
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historical_projections["Upper_68"],
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alpha=0.3,
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color="green",
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label="68% Confidence Interval",
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)
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# Customize y-axis
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ax.yaxis.set_major_formatter(plt.FuncFormatter(format_price))
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# Set custom y-axis ticks
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min_price = min(
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df["Low"].min(),
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historical_projections["Lower_95"].min(),
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0.0001, # Set minimum price floor
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)
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max_price = max(df["High"].max(), historical_projections["Upper_95"].max())
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price_points = get_nice_price_points(min_price, max_price)
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ax.set_yticks(price_points)
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# Add halving lines
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halving_dates = get_halving_dates()
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relevant_halvings = halving_dates[
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(halving_dates >= start_date) & (halving_dates <= validation_df["Date"].max())
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]
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for date in relevant_halvings:
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ax.axvline(date, color="red", linestyle="--", alpha=0.3)
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ax.text(
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date,
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ax.get_ylim()[1],
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"Halving",
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rotation=90,
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va="top",
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ha="right",
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alpha=0.7,
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)
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# Calculate and add model performance metrics
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if len(validation_df) > 0:
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# Create a common date range for comparison
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actual_prices = validation_df.set_index("Date")["Close"]
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common_dates = actual_prices.index.intersection(historical_projections.index)
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if len(common_dates) > 0:
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actual_aligned = actual_prices[common_dates]
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projections_aligned = historical_projections.loc[common_dates]
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# Calculate metrics using aligned data
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mape = (
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np.mean(
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np.abs(
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(actual_aligned - projections_aligned["Expected_Trend"])
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/ actual_aligned
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)
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)
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* 100
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)
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coverage_95 = (
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np.mean(
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(actual_aligned >= projections_aligned["Lower_95"])
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& (actual_aligned <= projections_aligned["Upper_95"])
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)
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* 100
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)
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coverage_68 = (
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np.mean(
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(actual_aligned >= projections_aligned["Lower_68"])
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& (actual_aligned <= projections_aligned["Upper_68"])
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)
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* 100
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)
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rmse = np.sqrt(
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np.mean((actual_aligned - projections_aligned["Expected_Trend"]) ** 2)
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)
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max_error = np.max(
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np.abs(actual_aligned - projections_aligned["Expected_Trend"])
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)
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# Add metrics to plot
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metrics_text = (
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f"Model Performance Metrics:\n"
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f"MAPE: {mape:.1f}%\n"
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f"RMSE: ${rmse:,.0f}\n"
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f"Max Error: ${max_error:,.0f}\n"
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f"95% CI Coverage: {coverage_95:.1f}%\n"
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f"68% CI Coverage: {coverage_68:.1f}%"
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)
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ax.text(
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0.02,
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0.98,
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metrics_text,
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transform=ax.transAxes,
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verticalalignment="top",
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bbox=dict(facecolor="white", alpha=0.8),
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)
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# Customize plot
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ax.set_title(
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f'Bitcoin Price: Model Backtest\nTraining: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")}'
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)
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ax.set_xlabel("Date")
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ax.set_ylabel("Price (USD)")
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ax.grid(True, which="major", linestyle="-", alpha=0.5)
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ax.grid(True, which="minor", linestyle=":", alpha=0.2)
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ax.legend(loc="center left", bbox_to_anchor=(1.02, 0.5))
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# Adjust layout and save
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plt.tight_layout()
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filename = f'bitcoin_backtest_{start_date.strftime("%Y%m%d")}_to_{backtest_date.strftime("%Y%m%d")}.png'
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plt.savefig(filename, dpi=300, bbox_inches="tight")
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plt.close()
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return historical_projections
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if __name__ == "__main__":
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analysis, df = analyze_bitcoin_prices("prices.csv")
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# create_plots(df) # Full history
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@@ -717,3 +920,59 @@ if __name__ == "__main__":
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projections = create_plots(df, start="2011-01-01", project_days=365 * 4)
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print("\nProjected Prices at Key Points:")
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print(projections.iloc[[29, 89, 179, 364]].round(2)) # 30, 90, 180, 365 days
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# Create multiple backtests for different periods
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backtests = [
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# First to second halving
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{
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"start_date": "2012-11-28", # First halving
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"backtest_date": "2016-07-09", # Second halving
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"project_days": 1460, # 4 years
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},
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# Second to third halving
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{
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"start_date": "2016-07-09", # Second halving
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"backtest_date": "2020-05-11", # Third halving
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"project_days": 1460, # 4 years
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},
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# Third halving onwards
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{
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"start_date": "2020-05-11", # Third halving
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"backtest_date": "2024-04-19", # Fourth halving (projected)
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"project_days": 1460, # 4 years
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},
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{
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"start_date": "2016-07-09", # Second halving
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"backtest_date": "2024-04-19", # Fourth halving (projected)
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"project_days": 1460, # 4 years
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},
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]
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# Run all backtests
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for params in backtests:
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print(
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f"\nRunning backtest from {params['start_date']} to {params['backtest_date']}"
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)
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backtest_projections = create_backtest_plot(df, **params)
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# Print some key projection points vs actual prices
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print("\nBacktest Results - Projected vs Actual Prices:")
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validation_df = df[df["Date"] > params["backtest_date"]]
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actual_prices = validation_df.set_index("Date")["Close"]
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for days in [30, 90, 180, 365]:
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target_date = pd.to_datetime(params["backtest_date"]) + pd.Timedelta(
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days=days
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)
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if (
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target_date in actual_prices.index
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and target_date in backtest_projections.index
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):
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projected = backtest_projections.loc[target_date]
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actual = actual_prices.loc[target_date]
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print(f"\n{days} days out ({target_date.strftime('%Y-%m-%d')}):")
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print(f"Actual Price: ${actual:,.2f}")
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print(f"Projected (Expected): ${projected['Expected_Trend']:,.2f}")
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print(
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f"Projected Range: ${projected['Lower_95']:,.2f} - ${projected['Upper_95']:,.2f}"
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)
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